{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shapefile\n",
    "sf=shapefile.Reader('./DBATP/DBATP_Polygon.shp')\n",
    "prts=sf.shapeRecords()[0].shape.points\n",
    "\n",
    "lon=[]\n",
    "lat=[]\n",
    "for i in range(len(prts)):\n",
    "    lon.append(prts[i][0])\n",
    "    lat.append(prts[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "lon=np.array(lon)\n",
    "lat=np.array(lat)\n",
    "west_lon=lon[lon<80]\n",
    "west_lat=lat[lon<80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8295,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4729"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(np.abs(lat-31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.00018882751472"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat[4729]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.3208160400391"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon[4729]  ##first point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2532"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(np.abs(lat[lon<80]-40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.40892892869253"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon[lon<80][2532]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([7152]),)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(lon==lon[lon<80][2532]) ##second point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "372"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(np.abs(lat[lon>100]-28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101.79031327394904"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon[lon>100][372]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([8146]),)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(lon==lon[lon>100][372]) ##third point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1186"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(np.abs(lon[lat>37]-101.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101.77806436925664"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon[lat>37][1186]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([7793]),)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(lon==lon[lat>37][1186]) ##fourth point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "west_lon=lon[4729:7152]\n",
    "west_lat=lat[4729:7152]\n",
    "south_lon=np.concatenate((lon[8146:],lon[0:4729]),axis=0)\n",
    "south_lat=np.concatenate((lat[8146:],lat[0:4729]),axis=0)\n",
    "north_lon=lon[7152:7793]\n",
    "north_lat=lat[7152:7793]\n",
    "east_lon=lon[7793:8146]\n",
    "east_lat=lat[7793:8146]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "ds=xr.open_dataset('./1979-2018q1q2.nc')\n",
    "grid=xr.full_like(ds['q1w'][0,0,:,:],np.nan)\n",
    "#grid=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;q1w&#x27; (lat: 61, lon: 141)&gt;\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    year     int32 1979\n",
       "    day      datetime64[ns] 2018-06-01\n",
       "  * lat      (lat) float32 40.0 39.75 39.5 39.25 39.0 ... 25.75 25.5 25.25 25.0\n",
       "  * lon      (lon) float32 70.0 70.25 70.5 70.75 ... 104.25 104.5 104.75 105.0</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'q1w' (lat: 61, lon: 141)>\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    year     int32 1979\n",
       "    day      datetime64[ns] 2018-06-01\n",
       "  * lat      (lat) float32 40.0 39.75 39.5 39.25 39.0 ... 25.75 25.5 25.25 25.0\n",
       "  * lon      (lon) float32 70.0 70.25 70.5 70.75 ... 104.25 104.5 104.75 105.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def closest(lat1,lon1,lat2,lon2):\n",
    "    i=np.argmin(np.abs(lat2-lat1))\n",
    "    j=np.argmin(np.abs(lon2-lon1))\n",
    "    return((i,j))\n",
    "\n",
    "for i in range(len(north_lat)):\n",
    "    j,k=closest(north_lat[i],north_lon[i],grid.lat,grid.lon)\n",
    "    grid[j,k]=1\n",
    "\n",
    "grid2=xr.full_like(grid,np.nan)\n",
    "for i in range(len(east_lat)):\n",
    "    j,k=closest(east_lat[i],east_lon[i],grid.lat,grid.lon)\n",
    "    grid2[j,k]=1\n",
    "grid3=xr.full_like(grid,np.nan)\n",
    "for i in range(len(south_lat)):\n",
    "    j,k=closest(south_lat[i],south_lon[i],grid.lat,grid.lon)\n",
    "    grid3[j,k]=1    \n",
    "grid4=xr.full_like(grid,np.nan)\n",
    "for i in range(len(west_lat)):\n",
    "    j,k=closest(west_lat[i],west_lon[i],grid.lat,grid.lon)\n",
    "    grid4[j,k]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 547.444x275.183 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 275,
       "width": 547
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import proplot as plot\n",
    "import cartopy.crs as ccrs\n",
    "import cmaps\n",
    "\n",
    "f,axs=plot.subplots(axwidth=5,proj={'cyl'})\n",
    "ax=axs\n",
    "#ax.plot(lon,lat,'k',transform=ccrs.PlateCarree())\n",
    "#ax.plot(west_lon,west_lat,'r',transform=ccrs.PlateCarree())\n",
    "#ax.plot(south_lon,south_lat,'c',transform=ccrs.PlateCarree())\n",
    "#ax.plot(east_lon,east_lat,'y',transform=ccrs.PlateCarree())\n",
    "#ax.plot(north_lon,north_lat,'b',transform=ccrs.PlateCarree())\n",
    "ax.pcolormesh(grid,cmap=cmaps.MPL_Blues,alpha=1,edgecolor='none')  #north\n",
    "ax.pcolormesh(grid2,cmap=cmaps.precip_diff_1lev,alpha=1,edgecolor='none')  #east\n",
    "ax.pcolormesh(grid3,cmap=cmaps.GMT_cool_r,alpha=1,edgecolor='none') #south\n",
    "ax.pcolormesh(grid4,cmap=cmaps.Cat12,alpha=1,edgecolor='none') #west\n",
    "\n",
    "ax.format(title='Boundary',labels=True,latlim=(25,41),lonlim=(70,105),\n",
    "          latlines=np.arange(25,42,3),lonlines=np.arange(70,106,5))\n",
    "f.savefig('fig7.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr.Dataset({'north':grid,'east':grid2,'south':grid3,'west':grid4}).to_netcdf('boundary.nc')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
